COVID-19-associated acute invasive fungal sinusitis: Clinical and imaging findings.

BACKGROUND AND PURPOSE: The purpose is to provide a comprehensive report describing the clinical and imaging features of Coronavirus disease 2019 (COVID-19)-related acute invasive fungal sinusitis (AIFS) and associated comorbidities. METHODS: A retrospective study was conducted on 25 patients (12 males and 13 females, mean age of 53.9±9.1 years). All patients had positive polymerase chain reaction test for COVID-19 and histopathological proof of AIFS. Patients underwent computed tomography (CT) and magnetic resonance examinations to assess sinonasal, orbital, and cranial spread. RESULTS: The most prevalent comorbidity among the study cohort was diabetes mellitus (DM). Twenty-one patients (84%) were diagnosed in the post-COVID-19 period after hospital discharge, with a mean interval of 19.1±9.2 days. Steroid treatment was given to 19 patients (76%). Orbital manifestations were the presenting symptoms in all patients, followed by facial edema, nasal discharge, and neurological symptoms. Sinonasal involvement ranged from mucosal thickening to complete sinus opacification by a predominant isodensity on CT, low T1, and high T2 signal intensity with variable enhancement patterns. Twenty-four patients had a unilateral orbital extension, and 12 patients showed signs of intracranial extension. Bone involvement was detected in 16 patients (64%). Follow-up scans in 18 patients (72%) showed rapid progression of the disease. Eight patients (32%) died, six from neurological complications and two from severe respiratory failure. CONCLUSION: Steroids, DM, and severe COVID-19 are the major risk factors of AIFS in the post-COVID-19 era. Imaging scans in all patients revealed different sinonasal, facial, orbital features, and intracranial involvement with rapid progression of the findings on follow-up scans.

Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection.

INTRODUCTION: Direct-acting antivirals (DAAs) represent a breakthrough in hepatitis C virus (HCV) treatment as they directly inhibit HCV nonstructural (NS) proteins (NS3/4A, NS5A, and NS5B). However, ongoing debates exist regarding their relationship with hepatocellular carcinoma (HCC) whose incidence is widely debated among investigators. This study was conducted to identify host pharmacogenetic factors that may influence HCC incidence upon using HCV DAAs. MATERIALS AND METHODS: Details regarding 16 HCV DAAs were collected from literature and DrugBank database. Digital structures of these drugs were fed into the pharmacogenomics/pharmacovigilance in - silico pipeline (PHARMIP) to predict the genetic factors that may underpin HCC development. RESULTS: We identified 184 unique genes and 40 unique variants that may have key answers for the DAA/HCC paradox. These findings could be used in different methods to aid in the precise application of HCV DAAs and minimize the proposed risk for HCC. All results could be accessed at: https://doi.org/10.17632/8ws8258hn3.2. DISCUSSION: All the identified factors are evidence related to HCC and significantly predicted by PHARMIP as DAA targets. We discuss some examples of the methods of using these results to address the DAA/HCC controversy based on the following three primary levels: 1 - individual DAA drug, 2 - DAA subclass, and 3 - the entire DAA class. Further wet laboratory investigation is required to evaluate these results.

PHARMIP: An insilico method to predict genetics that underpin adverse drug reactions.

Pharmacovigilance is the pharmacological science that focuses on the safe and appropriate use of drugs.Variability in response to drug therapy in both terms of safety and efficacy is highly related to patient's personal genomics. Hence, pharmacovigilance considers pharmacogenomics methodologies in the evaluation of medicinal products. The aim of this work is to introduce the pharmacovigilance/ pharmacogenomics insilico pipeline (PHARMIP) that uses the drug (or drug candidate) digital structure and the advances in bioinformatics tools and databases to figure-out the genetic factors underlying the drug reported adverse reactions (ADRs).PHARMIP uses user-friendly freely available bioinformatics resources to help pharmacovigilance and pharmacogenomics scientists with minimal bioinformatics experience to retrieve helpful information for their daily basis activities. Also, PHARMIP could help the advances in precision medicine in a drug-centric approach as it can be used to reveal genetic risk factors for certain drug ADRs. Domperidone was used as an example to the application of PHARMIP as the pipeline was initially developed during the insilico exploration of domperidone cardiotoxic ADRs. Method is composed of 3 main steps: •Preparing the drug off-label targets (OLT) list.•Retrieving the related diseases/ adverse reactions (DA) list.•Analysis of DA list to get answers.